Privacy-Preserving Computing: For Big Data Analytics and AI

Name: Privacy-Preserving Computing: For Big Data Analytics and AI
Brand: Cambridge University Press
SKU: 9781009299510
Price: 6645.0 INR
Availability: InStock

by Kai Chen

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ISBN13: 9781009299510
Binding: Hardcover
Subject: N/A
Publisher: Cambridge University Press
Publisher Imprint: Cambridge University Press
Publication Date: January 11, 2024
Pages: 271
Original Price: GBP 52.0
Language: English
Edition: N/A
Item Weight: 522 grams
BISAC Subject(s): Artificial Intelligence / Natural Language Processing and Security / General

About The Book

Privacy-preserving computing aims to protect the personal information of users while capitalizing on the possibilities unlocked by big data. This practical introduction for students, researchers, and industry practitioners is the first cohesive and systematic presentation of the field's advances over four decades. The book shows how to use privacy-preserving computing in real-world problems in data analytics and AI, and includes applications in statistics, database queries, and machine learning. The book begins by introducing cryptographic techniques such as secret sharing, homomorphic encryption, and oblivious transfer, and then broadens its focus to more widely applicable techniques such as differential privacy, trusted execution environment, and federated learning. The book ends with privacy-preserving computing in practice in areas like finance, online advertising, and healthcare, and finally offers a vision for the future of the field.

About The Author

Chen, Kai: - Kai Chen is Professor at the Department of Computer Science and Engineering of the Hong Kong University of Science and Technology, where he leads the Intelligent Networking and Systems (iSING) Lab and the WeChat-HKUST Joint Lab on Artificial Intelligence Technology. His research interests include data center networking, high-performance networking, machine learning systems, and hardware acceleration.

Yang, Qiang: - Qiang Yang is Chief Ai Officer at Webank and Professor Emeritus at the Department of Computer Science and Engineering of the Hong Kong University of Science and Technology. He is an AAAI, ACM, and IEEE Fellow and Fellow of the Canadian Royal Society. He has authored books such as 'Intelligent Planning, ' 'Crafting Your Research Future, ' 'Transfer Learning, ' and 'Federated Learning.' His research interests include artificial intelligence, machine learning and data mining, automated planning, transfer learning, and federated learning.

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